
"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase__			:							Any      =      16
lowerCamelCase__			:							int      =      32





def __A    ( a_			:				Optional[Any]		,		a_			:				Dict = 16  )->				Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE							:  List[Any]					=			AutoTokenizer.from_pretrained('''bert-base-cased'''  )
		SCREAMING_SNAKE_CASE							:  int					=			load_dataset('''glue'''		,		'''mrpc'''  )

		def tokenize_function(a_			:				Optional[Any]  ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE							:  Optional[int]					=			tokenizer(examples['''sentence1''']		,		examples['''sentence2''']		,		truncation=a__		,		max_length=a__  )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				SCREAMING_SNAKE_CASE							:  Dict					=			datasets.map(
				    a__		,		batched=a__		,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,		)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		SCREAMING_SNAKE_CASE							:  Optional[Any]					=			tokenized_datasets.rename_column('''label'''		,		'''labels'''  )

		def collate_fn(a_			:				Union[str, Any]  ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				SCREAMING_SNAKE_CASE							:  Tuple					=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						SCREAMING_SNAKE_CASE							:  List[Any]					=			16
				elif accelerator.mixed_precision != "no":
						SCREAMING_SNAKE_CASE							:  Dict					=			8
				else:
						SCREAMING_SNAKE_CASE							:  List[Any]					=			None

				return tokenizer.pad(
				    a__		,		padding='''longest'''		,		max_length=a__		,		pad_to_multiple_of=a__		,		return_tensors='''pt'''		,		)

		# Instantiate dataloaders.
		SCREAMING_SNAKE_CASE							:  Tuple					=			DataLoader(
		    tokenized_datasets['''train''']		,		shuffle=a__		,		collate_fn=a__		,		batch_size=a__  )
		SCREAMING_SNAKE_CASE							:  Optional[int]					=			DataLoader(
		    tokenized_datasets['''validation''']		,		shuffle=a__		,		collate_fn=a__		,		batch_size=a__  )

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					lowerCamelCase__			:							int      =      mocked_dataloaders  # noqa: F811





def __A    ( a_			:				Any		,		a_			:				Optional[Any]  )->				Any:




		'''simple docstring'''






		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,		a__  ) == "1":
				SCREAMING_SNAKE_CASE							:  Optional[Any]					=			2
		# New Code #
		SCREAMING_SNAKE_CASE							:  List[Any]					=			int(args.gradient_accumulation_steps  )
		SCREAMING_SNAKE_CASE							:  List[str]					=			int(args.local_sgd_steps  )
		# Initialize accelerator
		SCREAMING_SNAKE_CASE							:  Any					=			Accelerator(
		    cpu=args.cpu		,		mixed_precision=args.mixed_precision		,		gradient_accumulation_steps=a__  )
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''  )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE							:  Optional[Any]					=			config["""lr"""]
		SCREAMING_SNAKE_CASE							:  Any					=			int(config['''num_epochs''']  )
		SCREAMING_SNAKE_CASE							:  List[Any]					=			int(config['''seed''']  )
		SCREAMING_SNAKE_CASE							:  Union[str, Any]					=			int(config['''batch_size''']  )

		SCREAMING_SNAKE_CASE							:  Any					=			evaluate.load('''glue'''		,		'''mrpc'''  )

		set_seed(a__  )
		SCREAMING_SNAKE_CASE							:  List[Any]					=			get_dataloaders(a__		,		a__  )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE							:  int					=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,		return_dict=a__  )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		SCREAMING_SNAKE_CASE							:  Optional[int]					=			model.to(accelerator.device  )

		# Instantiate optimizer
		SCREAMING_SNAKE_CASE							:  str					=			AdamW(params=model.parameters()		,		lr=a__  )

		# Instantiate scheduler
		SCREAMING_SNAKE_CASE							:  Optional[int]					=			get_linear_schedule_with_warmup(
		    optimizer=a__		,		num_warmup_steps=1_00		,		num_training_steps=(len(a__  ) * num_epochs)		,		)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		SCREAMING_SNAKE_CASE							:  Dict					=			accelerator.prepare(
		    a__		,		a__		,		a__		,		a__		,		a__  )

		# Now we train the model
		for epoch in range(a__  ):
				model.train()
				with LocalSGD(
				    accelerator=a__		,		model=a__		,		local_sgd_steps=a__		,		enabled=local_sgd_steps is not None  ) as local_sgd:
						for step, batch in enumerate(a__  ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device  )
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(a__  ):
										SCREAMING_SNAKE_CASE							:  Optional[int]					=			model(**a__  )
										SCREAMING_SNAKE_CASE							:  Optional[int]					=			output.loss
										accelerator.backward(a__  )
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				model.eval()
				for step, batch in enumerate(a__  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device  )
						with torch.no_grad():
								SCREAMING_SNAKE_CASE							:  Dict					=			model(**a__  )
						SCREAMING_SNAKE_CASE							:  Optional[int]					=			outputs.logits.argmax(dim=-1  )
						SCREAMING_SNAKE_CASE							:  int					=			accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
						metric.add_batch(
						    predictions=a__		,		references=a__		,		)

				SCREAMING_SNAKE_CASE							:  Optional[int]					=			metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"		,		a__  )





def __A    ( )->				str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE							:  Any					=			argparse.ArgumentParser(description='''Simple example of training script.'''  )
		parser.add_argument(
		    '''--mixed_precision'''		,		type=a__		,		default=a__		,		choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,		help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.'''		,		)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps'''		,		type=a__		,		default=1		,		help='''The number of minibatches to be ran before gradients are accumulated.'''		,		)
		parser.add_argument(
		    '''--local_sgd_steps'''		,		type=a__		,		default=8		,		help='''Number of local SGD steps or None to disable local SGD'''  )
		parser.add_argument('''--cpu'''		,		action='''store_true'''		,		help='''If passed, will train on the CPU.'''  )
		SCREAMING_SNAKE_CASE							:  Dict					=			parser.parse_args()
		SCREAMING_SNAKE_CASE							:  Union[str, Any]					=			{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(a__		,		a__  )


if __name__ == "__main__":
					main()








import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=3  ,      _lowercase=4  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Union[str, Any] 							= parent
									a_						:						int 							= batch_size
									a_						:						int 							= seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_input_mask
									a_						:						str 							= use_token_type_ids
									a_						:						List[str] 							= use_labels
									a_						:						List[Any] 							= vocab_size
									a_						:						Dict 							= hidden_size
									a_						:						List[Any] 							= num_hidden_layers
									a_						:						str 							= num_attention_heads
									a_						:						str 							= intermediate_size
									a_						:						Optional[int] 							= hidden_act
									a_						:						Optional[int] 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[Any] 							= type_vocab_size
									a_						:						Any 							= type_sequence_label_size
									a_						:						List[Any] 							= initializer_range
									a_						:						List[str] 							= num_labels
									a_						:						Union[str, Any] 							= num_choices
									a_						:						Dict 							= scope





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Union[str, Any] 							= None
									if self.use_input_mask:
												a_						:						Optional[int] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[Any] 							= None
									if self.use_token_type_ids:
												a_						:						Any 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Union[str, Any] 							= None
									a_						:						Dict 							= None
									a_						:						Any 							= None
									if self.use_labels:
												a_						:						Tuple 							= ids_tensor([self.batch_size]  ,      self.type_sequence_label_size    )
												a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels    )
												a_						:						int 							= ids_tensor([self.batch_size]  ,      self.num_choices    )

									a_						:						Optional[int] 							= self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





						def 			UpperCamelCase__					(    self    )     -> Any:
									return OpenLlamaConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      use_stable_embedding=_lowercase  ,      )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Dict:
									a_						:						Optional[Any] 							= OpenLlamaModel(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						List[Any] 							= model(_lowercase  ,      attention_mask=_lowercase    )
									a_						:						Optional[Any] 							= model(_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Union[str, Any]:
									a_						:						int 							= True
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Optional[int] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
									a_						:						Any 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      )
									a_						:						Optional[int] 							= model(_lowercase  ,      attention_mask=_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Any:
									a_						:						Dict 							= OpenLlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						int 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[str]:
									a_						:						Dict 							= True
									a_						:						Optional[int] 							= True
									a_						:						Dict 							= OpenLlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()

									# first forward pass
									a_						:						List[str] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      use_cache=_lowercase  ,      )
									a_						:						Optional[Any] 							= outputs.past_key_values

									# create hypothetical multiple next token and extent to next_input_ids
									a_						:						Optional[Any] 							= ids_tensor((self.batch_size, 3)  ,      config.vocab_size    )
									a_						:						Union[str, Any] 							= ids_tensor((self.batch_size, 3)  ,      vocab_size=2    )

									# append to next input_ids and
									a_						:						Any 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )
									a_						:						Any 							= torch.cat([input_mask, next_mask]  ,      dim=-1    )

									a_						:						Optional[int] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]
									a_						:						Dict 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      past_key_values=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]

									# select random slice
									a_						:						Optional[int] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Dict 							= output_from_no_past[:, -3:, random_slice_idx].detach()
									a_						:						Optional[int] 							= output_from_past[:, :, random_slice_idx].detach()

									self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Any 							= self.prepare_config_and_inputs()
									(
									    (
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					
									)						:						Union[str, Any] 							= config_and_inputs
									a_						:						Tuple 							= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Tuple       					=			(
						    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
						)
						_A    :				int       					=			(OpenLlamaForCausalLM,) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(
						    {
						        '''feature-extraction''': OpenLlamaModel,
						        '''text-classification''': OpenLlamaForSequenceClassification,
						        '''text-generation''': OpenLlamaForCausalLM,
						        '''zero-shot''': OpenLlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_A    :				Tuple       					=			False
						_A    :				Any       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= OpenLlamaModelTester(self    )
									a_						:						Optional[int] 							= ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Union[str, Any] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Any 							= self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
												a_						:						Dict 							= type
												self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_							,					a_						:						Tuple 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Any 							= 3
									a_						:						List[str] 							= input_dict["""input_ids"""]
									a_						:						List[str] 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						int 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						int 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Tuple 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_							,					a_						:						Any 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Union[str, Any] 							= 3
									a_						:						List[Any] 							= """single_label_classification"""
									a_						:						Dict 							= input_dict["""input_ids"""]
									a_						:						int 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						List[str] 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						Any 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Any 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_							,					a_						:						Union[str, Any] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Any 							= 3
									a_						:						List[str] 							= """multi_label_classification"""
									a_						:						Dict 							= input_dict["""input_ids"""]
									a_						:						Any 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						List[Any] 							= ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]  ,      self.model_tester.type_sequence_label_size    ).to(torch.float    )
									a_						:						Optional[int] 							= OpenLlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						int 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""    )
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_							,					a_						:						Optional[int] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						List[Any] 							= ids_tensor([1, 10]  ,      config.vocab_size    )
									a_						:						str 							= ids_tensor([1, int(config.max_position_embeddings * 1.5    )]  ,      config.vocab_size    )

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									original_model.to(_lowercase    )
									original_model.eval()
									a_						:						Union[str, Any] 							= original_model(_lowercase    ).last_hidden_state
									a_						:						str 							= original_model(_lowercase    ).last_hidden_state

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						int 							= {"""type""": scaling_type, """factor""": 1_0.0}
									a_						:						Union[str, Any] 							= OpenLlamaModel(_lowercase    )
									scaled_model.to(_lowercase    )
									scaled_model.eval()
									a_						:						Optional[int] 							= scaled_model(_lowercase    ).last_hidden_state
									a_						:						Tuple 							= scaled_model(_lowercase    ).last_hidden_state

									# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
									# maximum sequence length, so the outputs for the short input should match.
									if scaling_type == "dynamic":
												self.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )
									else:
												self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )

									# The output should be different for long inputs
									self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )


